import seaborn
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from sklearn.metrics import r2_score, mean_squared_error
 
df = seaborn.load_dataset("mpg")
print(df.head())
print(df.info())
 
df = df.dropna()
X_train, X_test, y_train, y_test = train_test_split(df[["horsepower", "weight", "acceleration"]], df["mpg"], train_size=0.8, shuffle=True, random_state=1)
ElasticNet_regressor = ElasticNet()
ElasticNet_regressor.fit(X_train, y_train)
 
y_test_predicted = ElasticNet_regressor.predict(X_test)
 
r2 = r2_score(y_test, y_test_predicted)
rmse = mean_squared_error(y_test, y_test_predicted, squared=False)
 
print("R2: ", r2)
print("RMSE: ", rmse)

Firstly, we are loading the “mpg” dataset using the seaborn Python library. After that, we print the first few lines and information about the dataset. The output will be like the following:

    mpg  cylinders  displacement  ...  model_year  origin                       name
0  18.0          8         307.0  ...          70     usa  chevrolet chevelle malibu
1  15.0          8         350.0  ...          70     usa          buick skylark 320
2  18.0          8         318.0  ...          70     usa         plymouth satellite
3  16.0          8         304.0  ...          70     usa              amc rebel sst
4  17.0          8         302.0  ...          70     usa                ford torino
 
[5 rows x 9 columns]
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 9 columns):
#   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
0   mpg           398 non-null    float64
1   cylinders     398 non-null    int64  
2   displacement  398 non-null    float64
3   horsepower    392 non-null    float64
4   weight        398 non-null    int64  
5   acceleration  398 non-null    float64
6   model_year    398 non-null    int64  
7   origin        398 non-null    object 
8   name          398 non-null    object 
dtypes: float64(4), int64(3), object(2)
memory usage: 28.1+ KB

As we can see the horsepower variable contains null values (it contains 392 non-null values out of 398 rows), we use the dropna() function to drop the rows containing null values…